# AutoSIGHT: Automatic Eye Tracking-based System for Immediate Grading of Human experTise

arXiv: https://arxiv.org/abs/2508.01015 · HTML: https://mlsanity.com/paper/2508.01015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 76 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks](https://mlsanity.com/paper/2011.08891/) (2020)
- [Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps](https://mlsanity.com/paper/1902.04893/) (2019)
- [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning](https://mlsanity.com/paper/2112.00686/) (2021)
- [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://mlsanity.com/paper/2105.03492/) (2021)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Deep Representations for Iris, Face, and Fingerprint Spoofing Detection](https://mlsanity.com/paper/1410.1980/) (2014)
- [Iris Presentation Attack Detection: Where Are We Now?](https://mlsanity.com/paper/2006.13252/) (2020)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition](https://mlsanity.com/paper/2310.04541/) (2023)
- [An Experimental Study of Deep Convolutional Features For Iris Recognition](https://mlsanity.com/paper/1702.01334/) (2017)
- [Presentation Attack Detection for Cadaver Iris](https://mlsanity.com/paper/1807.04058/) (2018)
- [Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?](https://mlsanity.com/paper/2002.08916/) (2020)
- [The Value of AI Guidance in Human Examination of Synthetically-Generated Faces](https://mlsanity.com/paper/2208.10544/) (2022)
- [Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models](https://mlsanity.com/paper/2303.11969/) (2023)
- [Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes](https://mlsanity.com/paper/1809.00212/) (2018)
- [Measuring Teachers' Visual Expertise Using the Gaze Relational Index Based on Real-world Eye-tracking Data and Varying Velocity Thresholds](https://mlsanity.com/paper/2304.05143/) (2023)

## Built on by (most cited first, 1 of 1)

- [VISER: Visually-Informed System for Enhanced Robustness in Iris Presentation Attack Detection](https://mlsanity.com/paper/2603.17859/) (2026)

## Abstract (arXiv)

Can we teach machines to assess the expertise of humans solving visual tasks automatically based on eye tracking features? This paper proposes AutoSIGHT, Automatic System for Immediate Grading of Human experTise, that classifies expert and non-expert performers, and builds upon an ensemble of features extracted from eye tracking data while the performers were solving a visual task. Results on the task of iris Presentation Attack Detection (PAD) used for this study show that with a small evaluation window of just 5 seconds, AutoSIGHT achieves an average average Area Under the ROC curve performance of 0.751 in subject-disjoint train-test regime, indicating that such detection is viable. Furthermore, when a larger evaluation window of up to 30 seconds is available, the Area Under the ROC curve (AUROC) increases to 0.8306, indicating the model is effectively leveraging more information at a cost of slightly delayed decisions. This work opens new areas of research on how to incorporate the automatic weighing of human and machine expertise into human-AI pairing setups, which need to react dynamically to nonstationary expertise distribution between the human and AI players (e.g. when the experts need to be replaced, or the task at hand changes rapidly). Along with this paper, we offer the eye tracking data used in this study collected from 6 experts and 53 non-experts solving iris PAD visual task.